Overview

Dataset statistics

Number of variables27
Number of observations767
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory161.9 KiB
Average record size in memory216.2 B

Variable types

NUM20
CAT7

Warnings

name has a high cardinality: 722 distinct values High cardinality
state has a high cardinality: 51 distinct values High cardinality
saifi_nomed has a high cardinality: 542 distinct values High cardinality
caidi_nomed has a high cardinality: 704 distinct values High cardinality
ba has a high cardinality: 51 distinct values High cardinality
total_cust is highly correlated with total_mwh and 1 other fieldsHigh correlation
total_mwh is highly correlated with total_custHigh correlation
circuits is highly correlated with total_custHigh correlation
nm_mwh is highly correlated with pv_mwh and 1 other fieldsHigh correlation
pv_mwh is highly correlated with nm_mwhHigh correlation
wind_mwh is highly correlated with nm_mwh and 1 other fieldsHigh correlation
wind_pct is highly correlated with wind_mwhHigh correlation
nm_pct is highly correlated with pv_pctHigh correlation
pv_pct is highly correlated with nm_pctHigh correlation
pv_mwh is highly skewed (γ1 = 21.71589745) Skewed
wind_mwh is highly skewed (γ1 = 25.81232179) Skewed
nm_mwh is highly skewed (γ1 = 22.41508526) Skewed
dem_res_mwh is highly skewed (γ1 = 26.53946767) Skewed
wind_pct is highly skewed (γ1 = 22.52253849) Skewed
name is uniformly distributed Uniform
total_mwh has unique values Unique
voltage has 473 (61.7%) zeros Zeros
gen_mwh has 569 (74.2%) zeros Zeros
purchase_mwh has 85 (11.1%) zeros Zeros
pv_mwh has 507 (66.1%) zeros Zeros
wind_mwh has 663 (86.4%) zeros Zeros
nm_mwh has 503 (65.6%) zeros Zeros
ee_mwh has 467 (60.9%) zeros Zeros
dem_res_customers has 536 (69.9%) zeros Zeros
dem_res_mwh has 655 (85.4%) zeros Zeros
pv_pct has 507 (66.1%) zeros Zeros
wind_pct has 663 (86.4%) zeros Zeros
nm_pct has 503 (65.6%) zeros Zeros
ee_pct has 467 (60.9%) zeros Zeros
dem_res_pct has 655 (85.4%) zeros Zeros
dem_res_cust_pct has 536 (69.9%) zeros Zeros

Reproduction

Analysis started2020-11-29 16:15:33.924722
Analysis finished2020-11-29 16:17:05.515726
Duration1 minute and 31.59 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct722
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
PacifiCorp
 
6
Empire District Electric Co
 
4
Fall River Rural Elec Coop Inc
 
3
Southwestern Electric Power Co
 
3
Otter Tail Power Co
 
3
Other values (717)
748 
ValueCountFrequency (%) 
PacifiCorp60.8%
 
Empire District Electric Co40.5%
 
Fall River Rural Elec Coop Inc30.4%
 
Southwestern Electric Power Co30.4%
 
Otter Tail Power Co30.4%
 
Black Hills Power, Inc. d/b/a30.4%
 
Northern States Power Co - Minnesota30.4%
 
MidAmerican Energy Co30.4%
 
Southwestern Public Service Co20.3%
 
Indiana Michigan Power Co20.3%
 
Other values (712)73595.8%
 
2020-11-29T11:17:05.787338image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique689 ?
Unique (%)89.8%
2020-11-29T11:17:06.010205image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length40
Median length27
Mean length25.88787484
Min length10

state
Categorical

HIGH CARDINALITY

Distinct51
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
MN
 
50
TX
 
43
TN
 
37
OH
 
37
NC
 
32
Other values (46)
568 
ValueCountFrequency (%) 
MN506.5%
 
TX435.6%
 
TN374.8%
 
OH374.8%
 
NC324.2%
 
MO303.9%
 
KY303.9%
 
IN293.8%
 
GA273.5%
 
WI253.3%
 
Other values (41)42755.7%
 
2020-11-29T11:17:06.251937image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3 ?
Unique (%)0.4%
2020-11-29T11:17:06.439449image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

total_mwh
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct767
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2877815.056
Minimum2829
Maximum111955723
Zeros0
Zeros (%)0.0%
Memory size6.0 KiB
2020-11-29T11:17:06.634143image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2829
5-th percentile76915.6
Q1237437.5
median531404
Q31484748.5
95-th percentile13977822.2
Maximum111955723
Range111952894
Interquartile range (IQR)1247311

Descriptive statistics

Standard deviation8153766.99
Coefficient of variation (CV)2.833318622
Kurtosis65.29246089
Mean2877815.056
Median Absolute Deviation (MAD)374076
Skewness6.847968817
Sum2207284148
Variance6.648391613e+13
MonotocityNot monotonic
2020-11-29T11:17:06.836412image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
116530710.1%
 
11575310.1%
 
90965610.1%
 
77141510.1%
 
17643810.1%
 
42275710.1%
 
84916410.1%
 
86459010.1%
 
221317210.1%
 
72327610.1%
 
Other values (757)75798.7%
 
ValueCountFrequency (%) 
282910.1%
 
329410.1%
 
883110.1%
 
980910.1%
 
1701010.1%
 
ValueCountFrequency (%) 
11195572310.1%
 
8470019410.1%
 
6225960710.1%
 
5850559510.1%
 
5602720110.1%
 

total_cust
Real number (ℝ≥0)

HIGH CORRELATION

Distinct762
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132509.8331
Minimum41
Maximum5061483
Zeros0
Zeros (%)0.0%
Memory size6.0 KiB
2020-11-29T11:17:07.026567image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum41
5-th percentile3207.8
Q110953.5
median24152
Q365271.5
95-th percentile601167.3
Maximum5061483
Range5061442
Interquartile range (IQR)54318

Descriptive statistics

Standard deviation385328.9388
Coefficient of variation (CV)2.907927131
Kurtosis59.94885077
Mean132509.8331
Median Absolute Deviation (MAD)17403
Skewness6.647667265
Sum101635042
Variance1.484783911e+11
MonotocityNot monotonic
2020-11-29T11:17:07.259561image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1016320.3%
 
1469520.3%
 
1353220.3%
 
2293120.3%
 
1282620.3%
 
1958010.1%
 
2447910.1%
 
24917510.1%
 
44749310.1%
 
2705510.1%
 
Other values (752)75298.0%
 
ValueCountFrequency (%) 
4110.1%
 
34210.1%
 
35710.1%
 
48510.1%
 
49910.1%
 
ValueCountFrequency (%) 
506148310.1%
 
412039910.1%
 
273058810.1%
 
263529110.1%
 
257262410.1%
 

no
Real number (ℝ≥0)

Distinct722
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12300.2412
Minimum97
Maximum60839
Zeros0
Zeros (%)0.0%
Memory size6.0 KiB
2020-11-29T11:17:07.475518image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum97
5-th percentile1476.1
Q15896
median12268
Q316715
95-th percentile21926.7
Maximum60839
Range60742
Interquartile range (IQR)10819

Descriptive statistics

Standard deviation8559.434421
Coefficient of variation (CV)0.6958753314
Kurtosis8.382814562
Mean12300.2412
Median Absolute Deviation (MAD)5430
Skewness2.021330442
Sum9434285
Variance73263917.61
MonotocityIncreasing
2020-11-29T11:17:07.685421image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1435460.8%
 
586040.5%
 
1423230.4%
 
1378130.4%
 
1234130.4%
 
1954530.4%
 
1769830.4%
 
616930.4%
 
1771820.3%
 
502720.3%
 
Other values (712)73595.8%
 
ValueCountFrequency (%) 
9710.1%
 
10810.1%
 
15510.1%
 
16210.1%
 
21310.1%
 
ValueCountFrequency (%) 
6083910.1%
 
6063110.1%
 
5901310.1%
 
5748310.1%
 
5669710.1%
 

type
Categorical

Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
Cooperative
402 
Municipal
204 
Investor Owned
138 
Political Subdivision
 
21
State
 
2
ValueCountFrequency (%) 
Cooperative40252.4%
 
Municipal20426.6%
 
Investor Owned13818.0%
 
Political Subdivision212.7%
 
State20.3%
 
2020-11-29T11:17:07.916305image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-29T11:17:08.045708image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-29T11:17:08.245007image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length21
Median length11
Mean length11.26597132
Min length5

saidi_nomed
Real number (ℝ≥0)

Distinct753
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138.7787744
Minimum0.655
Maximum1239.3
Zeros0
Zeros (%)0.0%
Memory size6.0 KiB
2020-11-29T11:17:08.445227image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.655
5-th percentile11.6178
Q155.63
median99.8
Q3170.2595
95-th percentile395.214
Maximum1239.3
Range1238.645
Interquartile range (IQR)114.6295

Descriptive statistics

Standard deviation141.2249481
Coefficient of variation (CV)1.017626425
Kurtosis13.87560432
Mean138.7787744
Median Absolute Deviation (MAD)55.75
Skewness3.002113658
Sum106443.32
Variance19944.48598
MonotocityNot monotonic
2020-11-29T11:17:08.654409image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
9830.4%
 
10430.4%
 
43.2720.3%
 
20120.3%
 
98.420.3%
 
16020.3%
 
95.120.3%
 
61.5620.3%
 
68.6820.3%
 
9620.3%
 
Other values (743)74597.1%
 
ValueCountFrequency (%) 
0.65510.1%
 
1.39410.1%
 
1.83710.1%
 
2.210.1%
 
2.4110.1%
 
ValueCountFrequency (%) 
1239.310.1%
 
1225.310.1%
 
968.610.1%
 
909.7110.1%
 
812.910.1%
 

saifi_nomed
Categorical

HIGH CARDINALITY

Distinct542
Distinct (%)70.7%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
.
 
61
0.87
 
6
0.49
 
5
0.66
 
5
1.64
 
4
Other values (537)
686 
ValueCountFrequency (%) 
.618.0%
 
0.8760.8%
 
0.4950.7%
 
0.6650.7%
 
1.6440.5%
 
0.9440.5%
 
1.3340.5%
 
1.0340.5%
 
1.540.5%
 
1.1240.5%
 
Other values (532)66686.8%
 
2020-11-29T11:17:08.890126image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique429 ?
Unique (%)55.9%
2020-11-29T11:17:09.101564image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length5
Mean length4.235984355
Min length1

caidi_nomed
Categorical

HIGH CARDINALITY

Distinct704
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
.
 
61
100
 
2
75
 
2
80
 
2
98
 
1
Other values (699)
699 
ValueCountFrequency (%) 
.618.0%
 
10020.3%
 
7520.3%
 
8020.3%
 
9810.1%
 
97.46153810.1%
 
81.43589710.1%
 
66.02966810.1%
 
92.77526410.1%
 
101.9917710.1%
 
Other values (694)69490.5%
 
2020-11-29T11:17:09.839323image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique700 ?
Unique (%)91.3%
2020-11-29T11:17:10.094148image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length9
Mean length8.062581486
Min length1

circuits
Real number (ℝ≥0)

HIGH CORRELATION

Distinct270
Distinct (%)35.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean190.5423729
Minimum1
Maximum5619
Zeros0
Zeros (%)0.0%
Memory size6.0 KiB
2020-11-29T11:17:10.294151image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q130
median57
Q3123
95-th percentile850.4
Maximum5619
Range5618
Interquartile range (IQR)93

Descriptive statistics

Standard deviation475.7502888
Coefficient of variation (CV)2.496821477
Kurtosis43.3178554
Mean190.5423729
Median Absolute Deviation (MAD)35
Skewness5.799938951
Sum146146
Variance226338.3373
MonotocityNot monotonic
2020-11-29T11:17:10.483948image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
42141.8%
 
16121.6%
 
36121.6%
 
30121.6%
 
37111.4%
 
43111.4%
 
13101.3%
 
4191.2%
 
691.2%
 
891.2%
 
Other values (260)65885.8%
 
ValueCountFrequency (%) 
130.4%
 
210.1%
 
330.4%
 
450.7%
 
550.7%
 
ValueCountFrequency (%) 
561910.1%
 
446410.1%
 
350010.1%
 
342110.1%
 
310910.1%
 

voltage
Real number (ℝ≥0)

ZEROS

Distinct141
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.01434159
Minimum0
Maximum2787
Zeros473
Zeros (%)61.7%
Memory size6.0 KiB
2020-11-29T11:17:10.698042image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q319
95-th percentile167.7
Maximum2787
Range2787
Interquartile range (IQR)19

Descriptive statistics

Standard deviation205.1035109
Coefficient of variation (CV)4.362573291
Kurtosis88.89435487
Mean47.01434159
Median Absolute Deviation (MAD)0
Skewness8.609000658
Sum36060
Variance42067.45019
MonotocityNot monotonic
2020-11-29T11:17:10.899450image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
047361.7%
 
4162.1%
 
191.2%
 
1481.0%
 
1181.0%
 
2260.8%
 
1660.8%
 
660.8%
 
260.8%
 
960.8%
 
Other values (131)22329.1%
 
ValueCountFrequency (%) 
047361.7%
 
191.2%
 
260.8%
 
350.7%
 
4162.1%
 
ValueCountFrequency (%) 
278710.1%
 
248110.1%
 
200010.1%
 
170410.1%
 
141610.1%
 

nerc
Categorical

Distinct13
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
SERC
204 
RFC
127 
MRO
102 
WECC
101 
0
83 
Other values (8)
150 
ValueCountFrequency (%) 
SERC20426.6%
 
RFC12716.6%
 
MRO10213.3%
 
WECC10113.2%
 
08310.8%
 
SPP597.7%
 
NPCC303.9%
 
TRE293.8%
 
FRCC141.8%
 
MISO91.2%
 
Other values (3)91.2%
 
2020-11-29T11:17:11.104657image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.1%
2020-11-29T11:17:11.278781image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length3
Mean length3.241199478
Min length1

gen_mwh
Real number (ℝ)

ZEROS

Distinct199
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1603899.498
Minimum-512
Maximum126508512
Zeros569
Zeros (%)74.2%
Memory size6.0 KiB
2020-11-29T11:17:11.449474image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-512
5-th percentile0
Q10
median0
Q346.5
95-th percentile9439026
Maximum126508512
Range126509024
Interquartile range (IQR)46.5

Descriptive statistics

Standard deviation8019806.349
Coefficient of variation (CV)5.000192568
Kurtosis103.6198706
Mean1603899.498
Median Absolute Deviation (MAD)0
Skewness8.836816232
Sum1230190915
Variance6.431729388e+13
MonotocityNot monotonic
2020-11-29T11:17:11.667170image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
056974.2%
 
1908042610.1%
 
2596110.1%
 
27047810.1%
 
2114434310.1%
 
1602794910.1%
 
12611310.1%
 
79410.1%
 
657610.1%
 
162610.1%
 
Other values (189)18924.6%
 
ValueCountFrequency (%) 
-51210.1%
 
056974.2%
 
410.1%
 
2110.1%
 
3110.1%
 
ValueCountFrequency (%) 
12650851210.1%
 
8441693010.1%
 
6261230910.1%
 
6054897810.1%
 
5174717710.1%
 

purchase_mwh
Real number (ℝ≥0)

ZEROS

Distinct683
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1562233.284
Minimum0
Maximum56679822
Zeros85
Zeros (%)11.1%
Memory size6.0 KiB
2020-11-29T11:17:11.886648image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1159193
median442563
Q31078380
95-th percentile6722856.3
Maximum56679822
Range56679822
Interquartile range (IQR)919187

Descriptive statistics

Standard deviation4091160.083
Coefficient of variation (CV)2.618789476
Kurtosis65.33718524
Mean1562233.284
Median Absolute Deviation (MAD)342590
Skewness6.847499692
Sum1198232929
Variance1.673759083e+13
MonotocityNot monotonic
2020-11-29T11:17:12.167350image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
08511.1%
 
122537810.1%
 
5062910.1%
 
82057010.1%
 
50270510.1%
 
22639010.1%
 
5857910.1%
 
38740110.1%
 
279073310.1%
 
258750510.1%
 
Other values (673)67387.7%
 
ValueCountFrequency (%) 
08511.1%
 
57210.1%
 
375910.1%
 
1802610.1%
 
1948010.1%
 
ValueCountFrequency (%) 
5667982210.1%
 
4216468610.1%
 
3023056510.1%
 
2914734110.1%
 
2267531810.1%
 

ba
Categorical

HIGH CARDINALITY

Distinct51
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
0
191 
MISO
160 
PJM
82 
SWPP
60 
ERCO
29 
Other values (46)
245 
ValueCountFrequency (%) 
019124.9%
 
MISO16020.9%
 
PJM8210.7%
 
SWPP607.8%
 
ERCO293.8%
 
SOCO283.7%
 
BPAT253.3%
 
ISNE222.9%
 
AECI202.6%
 
WACM152.0%
 
Other values (41)13517.6%
 
2020-11-29T11:17:12.436543image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique20 ?
Unique (%)2.6%
2020-11-29T11:17:12.654090image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length3.050847458
Min length1

pv_mwh
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct257
Distinct (%)33.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1549.68204
Minimum0
Maximum466156.237
Zeros507
Zeros (%)66.1%
Memory size6.0 KiB
2020-11-29T11:17:12.834874image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q355.9255
95-th percentile1890.7489
Maximum466156.237
Range466156.237
Interquartile range (IQR)55.9255

Descriptive statistics

Standard deviation18568.04635
Coefficient of variation (CV)11.98184264
Kurtosis522.559854
Mean1549.68204
Median Absolute Deviation (MAD)0
Skewness21.71589745
Sum1188606.125
Variance344772345.3
MonotocityNot monotonic
2020-11-29T11:17:13.036262image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
050766.1%
 
9.16720.3%
 
264.25720.3%
 
0.2620.3%
 
14520.3%
 
215.72210.1%
 
1253.2710.1%
 
3725.4610.1%
 
598.1910.1%
 
1627710.1%
 
Other values (247)24732.2%
 
ValueCountFrequency (%) 
050766.1%
 
0.00810.1%
 
0.0110.1%
 
0.01310.1%
 
0.03810.1%
 
ValueCountFrequency (%) 
466156.23710.1%
 
169882.50910.1%
 
86299.75210.1%
 
71653.48610.1%
 
4766910.1%
 

wind_mwh
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct103
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.94687353
Minimum0
Maximum49255.057
Zeros663
Zeros (%)86.4%
Memory size6.0 KiB
2020-11-29T11:17:13.259494image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile14.3675
Maximum49255.057
Range49255.057
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1827.784027
Coefficient of variation (CV)21.26643997
Kurtosis688.1331473
Mean85.94687353
Median Absolute Deviation (MAD)0
Skewness25.81232179
Sum65921.252
Variance3340794.448
MonotocityNot monotonic
2020-11-29T11:17:13.496173image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
066386.4%
 
26.70720.3%
 
0.09520.3%
 
0.2610.1%
 
1474.70410.1%
 
8.9210.1%
 
0.82210.1%
 
1.2210.1%
 
0.0810.1%
 
0.02910.1%
 
Other values (93)9312.1%
 
ValueCountFrequency (%) 
066386.4%
 
0.00410.1%
 
0.0210.1%
 
0.02210.1%
 
0.02610.1%
 
ValueCountFrequency (%) 
49255.05710.1%
 
11648.21310.1%
 
1474.70410.1%
 
62510.1%
 
322.20910.1%
 

nm_mwh
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct261
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1643.192293
Minimum0
Maximum515411.294
Zeros503
Zeros (%)65.6%
Memory size6.0 KiB
2020-11-29T11:17:13.729513image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q365.2265
95-th percentile1924.3
Maximum515411.294
Range515411.294
Interquartile range (IQR)65.2265

Descriptive statistics

Standard deviation20219.44585
Coefficient of variation (CV)12.30497851
Kurtosis552.4472018
Mean1643.192293
Median Absolute Deviation (MAD)0
Skewness22.41508526
Sum1260328.489
Variance408825990.6
MonotocityNot monotonic
2020-11-29T11:17:13.981978image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
050365.6%
 
264.35220.3%
 
0.2620.3%
 
14520.3%
 
35.87420.3%
 
36556.82310.1%
 
19412.84510.1%
 
2493.9910.1%
 
72242.21910.1%
 
50.59510.1%
 
Other values (251)25132.7%
 
ValueCountFrequency (%) 
050365.6%
 
0.00810.1%
 
0.0110.1%
 
0.01310.1%
 
0.03810.1%
 
ValueCountFrequency (%) 
515411.29410.1%
 
169882.50910.1%
 
86299.75210.1%
 
72242.21910.1%
 
4766910.1%
 

ee_mwh
Real number (ℝ≥0)

ZEROS

Distinct300
Distinct (%)39.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27549.04185
Minimum0
Maximum2097423
Zeros467
Zeros (%)60.9%
Memory size6.0 KiB
2020-11-29T11:17:14.326863image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31209
95-th percentile165413.5943
Maximum2097423
Range2097423
Interquartile range (IQR)1209

Descriptive statistics

Standard deviation125573.9753
Coefficient of variation (CV)4.55819756
Kurtosis118.6923627
Mean27549.04185
Median Absolute Deviation (MAD)0
Skewness9.237865885
Sum21130115.1
Variance1.576882328e+10
MonotocityNot monotonic
2020-11-29T11:17:14.596346image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
046760.9%
 
70120.3%
 
34310.1%
 
88.1710.1%
 
72210.1%
 
37910.1%
 
197569.8610.1%
 
10957.810.1%
 
8.38210.1%
 
6189.04610.1%
 
Other values (290)29037.8%
 
ValueCountFrequency (%) 
046760.9%
 
0.00210.1%
 
0.410.1%
 
1.30910.1%
 
2.33910.1%
 
ValueCountFrequency (%) 
209742310.1%
 
132204510.1%
 
94460810.1%
 
71819610.1%
 
696831.7310.1%
 

dem_res_customers
Real number (ℝ≥0)

ZEROS

Distinct213
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12274.06258
Minimum0
Maximum895133
Zeros536
Zeros (%)69.9%
Memory size6.0 KiB
2020-11-29T11:17:14.841758image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q331.5
95-th percentile36601.2
Maximum895133
Range895133
Interquartile range (IQR)31.5

Descriptive statistics

Standard deviation69746.80701
Coefficient of variation (CV)5.682454896
Kurtosis83.93998013
Mean12274.06258
Median Absolute Deviation (MAD)0
Skewness8.622895747
Sum9414206
Variance4864617087
MonotocityNot monotonic
2020-11-29T11:17:15.066309image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
053669.9%
 
460.8%
 
150.7%
 
540.5%
 
230.4%
 
820.3%
 
1920.3%
 
2020.3%
 
2791220.3%
 
62920.3%
 
Other values (203)20326.5%
 
ValueCountFrequency (%) 
053669.9%
 
150.7%
 
230.4%
 
460.8%
 
540.5%
 
ValueCountFrequency (%) 
89513310.1%
 
75820710.1%
 
72442410.1%
 
69372510.1%
 
48557610.1%
 

dem_res_mwh
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct110
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1751.307789
Minimum0
Maximum977215
Zeros655
Zeros (%)85.4%
Memory size6.0 KiB
2020-11-29T11:17:15.335951image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile302.71
Maximum977215
Range977215
Interquartile range (IQR)0

Descriptive statistics

Standard deviation35827.02422
Coefficient of variation (CV)20.45729737
Kurtosis720.5853244
Mean1751.307789
Median Absolute Deviation (MAD)0
Skewness26.53946767
Sum1343253.074
Variance1283575664
MonotocityNot monotonic
2020-11-29T11:17:15.603101image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
065585.4%
 
1030.4%
 
4720.3%
 
649.80410.1%
 
74410.1%
 
4.510.1%
 
134.6410.1%
 
1.54910.1%
 
140.88210.1%
 
204.3910.1%
 
Other values (100)10013.0%
 
ValueCountFrequency (%) 
065585.4%
 
0.00510.1%
 
0.110.1%
 
0.65710.1%
 
0.68910.1%
 
ValueCountFrequency (%) 
97721510.1%
 
16260610.1%
 
33816.2110.1%
 
3170410.1%
 
2836810.1%
 

pv_pct
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct261
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06329298997
Minimum0
Maximum10.63045152
Zeros507
Zeros (%)66.1%
Memory size6.0 KiB
2020-11-29T11:17:15.868053image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.007546905041
95-th percentile0.1597437484
Maximum10.63045152
Range10.63045152
Interquartile range (IQR)0.007546905041

Descriptive statistics

Standard deviation0.4962307468
Coefficient of variation (CV)7.84021654
Kurtosis325.1000385
Mean0.06329298997
Median Absolute Deviation (MAD)0
Skewness16.95503325
Sum48.54572331
Variance0.2462449541
MonotocityNot monotonic
2020-11-29T11:17:16.095751image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
050766.1%
 
0.00102133881310.1%
 
0.010204793910.1%
 
0.000396435730310.1%
 
0.0312691983710.1%
 
0.123617613710.1%
 
0.0291700938210.1%
 
0.218250450410.1%
 
0.015857603610.1%
 
0.344026823410.1%
 
Other values (251)25132.7%
 
ValueCountFrequency (%) 
050766.1%
 
1.762774296e-0710.1%
 
3.142061705e-0610.1%
 
7.697710893e-0610.1%
 
9.501479307e-0610.1%
 
ValueCountFrequency (%) 
10.6304515210.1%
 
7.17605928410.1%
 
2.64636489310.1%
 
2.26120063710.1%
 
2.16369021210.1%
 

wind_pct
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct105
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.002523286726
Minimum0
Maximum0.7582376487
Zeros663
Zeros (%)86.4%
Memory size6.0 KiB
2020-11-29T11:17:16.316091image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.002034468338
Maximum0.7582376487
Range0.7582376487
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.02955500793
Coefficient of variation (CV)11.71290112
Kurtosis562.9827451
Mean0.002523286726
Median Absolute Deviation (MAD)0
Skewness22.52253849
Sum1.935360919
Variance0.0008734984935
MonotocityNot monotonic
2020-11-29T11:17:16.514156image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
066386.4%
 
0.0190669433710.1%
 
0.0244944399110.1%
 
0.00155428613110.1%
 
0.00163575295410.1%
 
0.00146715800810.1%
 
0.000733210935310.1%
 
0.00287042295710.1%
 
0.0698760202810.1%
 
0.000308431843810.1%
 
Other values (95)9512.4%
 
ValueCountFrequency (%) 
066386.4%
 
2.380995335e-0710.1%
 
5.119760632e-0710.1%
 
5.660150418e-0710.1%
 
2.189482456e-0610.1%
 
ValueCountFrequency (%) 
0.758237648710.1%
 
0.19969135710.1%
 
0.141959175810.1%
 
0.100645352310.1%
 
0.0729405820810.1%
 

nm_pct
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct265
Distinct (%)34.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06694010457
Minimum0
Maximum10.63045152
Zeros503
Zeros (%)65.6%
Memory size6.0 KiB
2020-11-29T11:17:16.741260image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.008334912244
95-th percentile0.172641644
Maximum10.63045152
Range10.63045152
Interquartile range (IQR)0.008334912244

Descriptive statistics

Standard deviation0.5122189637
Coefficient of variation (CV)7.651899664
Kurtosis309.9899077
Mean0.06694010457
Median Absolute Deviation (MAD)0
Skewness16.64730555
Sum51.34306021
Variance0.2623682668
MonotocityNot monotonic
2020-11-29T11:17:16.963473image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
050365.6%
 
0.0840075648610.1%
 
0.0592867893610.1%
 
0.0081620685810.1%
 
0.0263865256110.1%
 
0.0767115245210.1%
 
0.000428479125810.1%
 
0.00253272061910.1%
 
0.000295491481910.1%
 
0.010204793910.1%
 
Other values (255)25533.2%
 
ValueCountFrequency (%) 
050365.6%
 
1.762774296e-0710.1%
 
3.142061705e-0610.1%
 
7.697710893e-0610.1%
 
9.501479307e-0610.1%
 
ValueCountFrequency (%) 
10.6304515210.1%
 
7.93429693310.1%
 
2.64640270610.1%
 
2.26120063710.1%
 
2.16645991810.1%
 

ee_pct
Real number (ℝ≥0)

ZEROS

Distinct301
Distinct (%)39.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3685372872
Minimum0
Maximum10.72710265
Zeros467
Zeros (%)60.9%
Memory size6.0 KiB
2020-11-29T11:17:17.169337image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.2373126344
95-th percentile1.683585311
Maximum10.72710265
Range10.72710265
Interquartile range (IQR)0.2373126344

Descriptive statistics

Standard deviation1.061427239
Coefficient of variation (CV)2.880108136
Kurtosis38.47378105
Mean0.3685372872
Median Absolute Deviation (MAD)0
Skewness5.537040118
Sum282.6680993
Variance1.126627785
MonotocityNot monotonic
2020-11-29T11:17:17.372724image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
046760.9%
 
0.343999109910.1%
 
1.2164222710.1%
 
3.80537269110.1%
 
0.0180142071610.1%
 
0.360924641210.1%
 
0.126787998910.1%
 
0.00699365228910.1%
 
4.1572471210.1%
 
0.974396136610.1%
 
Other values (291)29137.9%
 
ValueCountFrequency (%) 
046760.9%
 
1.333502244e-0610.1%
 
5.771817049e-0510.1%
 
8.970168583e-0510.1%
 
0.00011108354110.1%
 
ValueCountFrequency (%) 
10.7271026510.1%
 
10.440228510.1%
 
8.15012512910.1%
 
7.83745344110.1%
 
7.8103179310.1%
 

dem_res_pct
Real number (ℝ≥0)

ZEROS

Distinct113
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01813022599
Minimum0
Maximum3.50953437
Zeros655
Zeros (%)85.4%
Memory size6.0 KiB
2020-11-29T11:17:17.613005image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.0097691566
Maximum3.50953437
Range3.50953437
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1799300655
Coefficient of variation (CV)9.924314542
Kurtosis252.2670477
Mean0.01813022599
Median Absolute Deviation (MAD)0
Skewness14.95854406
Sum13.90588334
Variance0.03237482847
MonotocityNot monotonic
2020-11-29T11:17:17.901911image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
065585.4%
 
0.00128023995410.1%
 
0.00075553451610.1%
 
0.00337408152410.1%
 
0.00676338805810.1%
 
0.113661169310.1%
 
0.00141244334310.1%
 
0.170930803310.1%
 
0.00118057409910.1%
 
0.00702553077910.1%
 
Other values (103)10313.4%
 
ValueCountFrequency (%) 
065585.4%
 
7.320194073e-0710.1%
 
3.664439909e-0510.1%
 
4.458969131e-0510.1%
 
6.927348433e-0510.1%
 
ValueCountFrequency (%) 
3.5095343710.1%
 
2.64901608410.1%
 
1.32237209210.1%
 
1.29877035110.1%
 
1.03946862410.1%
 

dem_res_cust_pct
Real number (ℝ≥0)

ZEROS

Distinct230
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.560794711
Minimum0
Maximum100
Zeros536
Zeros (%)69.9%
Memory size6.0 KiB
2020-11-29T11:17:18.184551image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.03561879445
95-th percentile31.608234
Maximum100
Range100
Interquartile range (IQR)0.03561879445

Descriptive statistics

Standard deviation13.82039082
Coefficient of variation (CV)3.03025935
Kurtosis21.52069424
Mean4.560794711
Median Absolute Deviation (MAD)0
Skewness4.351490243
Sum3498.129543
Variance191.0032023
MonotocityNot monotonic
2020-11-29T11:17:18.414475image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
053669.9%
 
10030.4%
 
7.9096045210.1%
 
5.92050387310.1%
 
25.619184910.1%
 
12.6928148410.1%
 
2.55099971610.1%
 
1.52623515910.1%
 
3.27722866710.1%
 
0.0160117846710.1%
 
Other values (220)22028.7%
 
ValueCountFrequency (%) 
053669.9%
 
0.000187605293510.1%
 
0.000255103993110.1%
 
0.000688416023610.1%
 
0.000690907859810.1%
 
ValueCountFrequency (%) 
10030.4%
 
99.9962574110.1%
 
97.141277310.1%
 
85.523385310.1%
 
84.3035866110.1%
 

Interactions

2020-11-29T11:15:37.313673image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-29T11:15:37.530137image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-29T11:15:37.781757image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-29T11:15:37.962456image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-29T11:15:38.134480image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-29T11:15:38.299567image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-29T11:15:38.480330image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-29T11:15:38.653907image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-29T11:15:38.820435image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-29T11:15:39.095843image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-29T11:15:39.363306image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-29T11:15:39.541668image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-29T11:15:39.797538image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-29T11:15:39.976450image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-29T11:15:40.147342image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-29T11:15:40.353684image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-29T11:15:40.539319image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-29T11:15:40.724678image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-29T11:15:40.933924image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-29T11:15:41.168846image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-29T11:15:41.333778image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-29T11:15:41.503401image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-29T11:15:41.670821image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-29T11:15:41.862689image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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Correlations

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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-11-29T11:17:19.142951image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-11-29T11:17:19.595366image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-11-29T11:17:20.008269image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-11-29T11:17:20.377499image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-11-29T11:17:03.865649image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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Sample

First rows

namestatetotal_mwhtotal_custnotypesaidi_nomedsaifi_nomedcaidi_nomedcircuitsvoltagenercgen_mwhpurchase_mwhbapv_mwhwind_mwhnm_mwhee_mwhdem_res_customersdem_res_mwhpv_pctwind_pctnm_pctee_pctdem_res_pctdem_res_cust_pct
0Adams Electric CoopIL176102.08931.097.0Cooperative169.7401.29131.581447.047.0SERC4612.0183534.0MISO0.0000.0000.0000.04402.00.000.0000000.0000000.0000000.0000000.00000049.288993
1Adams-Columbia Electric CoopWI515186.037607.0108.0Cooperative139.9000.95147.2631695.016.0MRO0.0545904.0MISO705.77522.235728.0100.04781.013647.360.1369940.0043160.1413100.0000002.64901612.713059
2Agralite Electric CoopMN238558.05251.0155.0Cooperative128.7591.181109.025464.00.0MRO447.0253275.0MISO0.0000.0000.0000.00.00.000.0000000.0000000.0000000.0000000.0000000.000000
3Aiken Electric Coop IncSC938436.048059.0162.0Cooperative97.720..122.00.0SERC0.0980124.0SC354.4560.000354.4563.80.00.000.0377710.0000000.0377710.0004050.0000000.000000
4Alaska Electric Light&Power CoAK337796.017280.0213.0Investor Owned79.0002.0837.98076931.031.0AK123203.0235572.000.0000.0000.0000.02386.00.000.0000000.0000000.0000000.0000000.00000013.807870
5City of Albemarle - (NC)NC282568.012072.0232.0Municipal44.0500.5383.11320814.014.0SERC0.0293574.000.0000.0000.0000.00.00.000.0000000.0000000.0000000.0000000.0000000.000000
6Albertville Municipal Utilities BoardAL604222.010277.0241.0Municipal21.6240.41851.73205722.00.0SERC0.0623425.000.0000.0000.0000.00.00.000.0000000.0000000.0000000.0000000.0000000.000000
7Alger-Delta Coop Electric AssnMI75981.010089.0305.0Cooperative55.8520.117477.3675217.00.0MISO0.085152.0MISO0.0000.0000.0000.00.00.000.0000000.0000000.0000000.0000000.0000000.000000
8Allamakee-Clayton El Coop, IncIA133395.09987.0329.0Cooperative141.6000.965146.7357553.00.0MRO148.0150540.0MISO1035.7490.1441035.8931124.02768.00.000.7764530.0001080.7765610.8426100.00000027.716031
9Altamaha Electric Member CorpGA440677.020790.0407.0Cooperative95.1000.47202.3404357.057.0SERC0.0451195.0SOCO0.0000.0000.0000.0256.014.000.0000000.0000000.0000000.0000000.0031771.231361

Last rows

namestatetotal_mwhtotal_custnotypesaidi_nomedsaifi_nomedcaidi_nomedcircuitsvoltagenercgen_mwhpurchase_mwhbapv_mwhwind_mwhnm_mwhee_mwhdem_res_customersdem_res_mwhpv_pctwind_pctnm_pctee_pctdem_res_pctdem_res_cust_pct
757Southwest Iowa Rural Elec CoopIA108165.05915.049986.0Cooperative121.7601.68772.17545916.00.0RFC0.0118904.000.0000.0000.0000.0000.00.0000.0000000.0000000.0000000.0000000.0000000.000000
758NSTAR Electric CompanyMA6495992.0696258.054913.0Investor Owned70.3000.73595.6462592259.0524.000.00.0ISNE466156.23749255.057515411.294696831.7308079.04.5007.1760590.7582387.93429710.7271030.0000691.160346
759Entergy Texas Inc.TX19008103.0459190.055937.0Investor Owned297.0002.019147.10253671.00.0SERC6326958.015088727.0MISO0.0000.0000.00044488.2398.073.7530.0000000.0000000.0000000.2340490.0003880.001742
760Heart of Texas Electric CoopTX474367.022155.055982.0Cooperative167.1501.33125.6766961.00.000.00.0ERCO0.0000.0000.0000.0000.00.0000.0000000.0000000.0000000.0000000.0000000.000000
761Black Hills Colorado Electric, LLCCO1954358.097890.056146.0Investor Owned73.6691.30756.364958127.00.000.00.0PSCO0.0000.0000.00015726.0000.00.0000.0000000.0000000.0000000.8046630.0000000.000000
762Ameren Illinois CompanyIL8017902.0499059.056697.0Investor Owned132.2900.995132.954772966.00.000.00.0MISO0.0000.0000.000333324.0000.00.0000.0000000.0000000.0000004.1572470.0000000.000000
763Liberty UtilitiesCA564462.048770.057483.0Investor Owned416.5102.96140.7128442.00.0WECC131026.0456184.0CISO0.0000.0000.0001779.1390.00.0000.0000000.0000000.0000000.3151920.0000000.000000
764PUD No 1 of Jefferson CountyWA376212.019742.059013.0Political Subdivision291.0002.04142.6470621.021.000.00.0BPAT0.0000.0000.000651.0000.00.0000.0000000.0000000.0000000.1730410.0000000.000000
765Upper Michigan Energy Resources Corp.MI1610240.036818.060631.0Investor Owned238.0001.5158.6666757.00.000.00.0MISO0.0000.0000.0000.00080.00.0000.0000000.0000000.0000000.0000000.0000000.217285
766Southern Pioneer Electric CompanyKS817910.016939.060839.0Cooperative106.410..83.00.000.00.0SWPP41.0420.00041.0420.0000.00.0000.0050180.0000000.0050180.0000000.0000000.000000